{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 加上每周数据\n",
    "## 数据进行归一化\n",
    "## 模型换成kaggle神器\n",
    "## 要求：就是提升到前一百名"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sys.version_info(major=3, minor=6, micro=6, releaselevel='final', serial=0)"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "pd.set_option('display.max_rows', 500)\n",
    "pd.set_option('display.max_columns', 100)\n",
    "\n",
    "import gc\n",
    "import time\n",
    "import pickle\n",
    "import seaborn as sns\n",
    "from tqdm import tqdm\n",
    "from itertools import product\n",
    "import matplotlib.pyplot as plt\n",
    "from xgboost import XGBRegressor\n",
    "from sklearn import preprocessing \n",
    "from xgboost import plot_importance\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "def plot_features(booster, figsize):    \n",
    "    fig, ax = plt.subplots(1,1,figsize=figsize)\n",
    "    return plot_importance(booster=booster, ax=ax)\n",
    "\n",
    "def downcast_dtypes(df):\n",
    "    float_cols = [c for c in df if df[c].dtype == \"float64\"]\n",
    "    int_cols = [c for c in df if df[c].dtype in [\"int64\", \"int32\"]]\n",
    "    df[float_cols] = df[float_cols].astype(np.float16)\n",
    "    df[int_cols] = df[int_cols].astype(np.int16)\n",
    "    return df\n",
    "\n",
    "import sys\n",
    "sys.version_info"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 读入保存好的数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(7756739, 41)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date_block_num</th>\n",
       "      <th>shop_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>item_cnt_month</th>\n",
       "      <th>shop_city</th>\n",
       "      <th>shop_name1</th>\n",
       "      <th>shop_type</th>\n",
       "      <th>item_category_id</th>\n",
       "      <th>name_1</th>\n",
       "      <th>name_2</th>\n",
       "      <th>name_3</th>\n",
       "      <th>item_type</th>\n",
       "      <th>item_subtype</th>\n",
       "      <th>item_cnt_month_lag_1</th>\n",
       "      <th>item_cnt_month_lag_2</th>\n",
       "      <th>item_cnt_month_lag_3</th>\n",
       "      <th>date_block_num_avg_item_cnt_lag_1</th>\n",
       "      <th>date_block_num_and_item_id_avg_item_cnt_lag_1</th>\n",
       "      <th>date_block_num_and_item_id_avg_item_cnt_lag_2</th>\n",
       "      <th>date_block_num_and_item_id_avg_item_cnt_lag_3</th>\n",
       "      <th>date_block_num_and_shop_id_avg_item_cnt_lag_1</th>\n",
       "      <th>date_block_num_and_shop_id_avg_item_cnt_lag_2</th>\n",
       "      <th>date_block_num_and_shop_id_avg_item_cnt_lag_3</th>\n",
       "      <th>date_block_num_and_shop_city_avg_item_cnt_lag_1</th>\n",
       "      <th>date_block_num_and_shop_name1_avg_item_cnt_lag_1</th>\n",
       "      <th>date_block_num_and_shop_type_avg_item_cnt_lag_1</th>\n",
       "      <th>date_block_num_and_item_category_id_avg_item_cnt_lag_1</th>\n",
       "      <th>date_block_num_and_item_type_avg_item_cnt_lag_1</th>\n",
       "      <th>date_block_num_and_item_subtype_avg_item_cnt_lag_1</th>\n",
       "      <th>date_block_num_and_shop_id_and_item_id_avg_item_cnt_lag_1</th>\n",
       "      <th>date_block_num_and_shop_id_and_name_1_avg_item_cnt_lag_1</th>\n",
       "      <th>date_block_num_and_shop_id_and_name_2_avg_item_cnt_lag_1</th>\n",
       "      <th>date_block_num_and_shop_id_and_name_3_avg_item_cnt_lag_1</th>\n",
       "      <th>date_block_num_and_shop_id_and_item_category_id_avg_item_cnt_lag_1</th>\n",
       "      <th>delta_price_lag</th>\n",
       "      <th>item_shop_last_sale</th>\n",
       "      <th>item_last_sale</th>\n",
       "      <th>item_first_sale</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>days</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1103919</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>27</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>19</td>\n",
       "      <td>27</td>\n",
       "      <td>77</td>\n",
       "      <td>42</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.292969</td>\n",
       "      <td>0.027771</td>\n",
       "      <td>0.083313</td>\n",
       "      <td>0.083313</td>\n",
       "      <td>0.076233</td>\n",
       "      <td>0.095459</td>\n",
       "      <td>0.063599</td>\n",
       "      <td>0.076233</td>\n",
       "      <td>0.076233</td>\n",
       "      <td>0.29126</td>\n",
       "      <td>0.743652</td>\n",
       "      <td>0.616699</td>\n",
       "      <td>0.682129</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.564453</td>\n",
       "      <td>0.096985</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.345459</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2013</td>\n",
       "      <td>4</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103920</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>28</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>30</td>\n",
       "      <td>28</td>\n",
       "      <td>108</td>\n",
       "      <td>42</td>\n",
       "      <td>8</td>\n",
       "      <td>55</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.292969</td>\n",
       "      <td>0.111084</td>\n",
       "      <td>0.083313</td>\n",
       "      <td>0.194458</td>\n",
       "      <td>0.076233</td>\n",
       "      <td>0.095459</td>\n",
       "      <td>0.063599</td>\n",
       "      <td>0.076233</td>\n",
       "      <td>0.076233</td>\n",
       "      <td>0.29126</td>\n",
       "      <td>1.081055</td>\n",
       "      <td>1.048828</td>\n",
       "      <td>1.081055</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.347900</td>\n",
       "      <td>0.096985</td>\n",
       "      <td>0.399902</td>\n",
       "      <td>0.259521</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2013</td>\n",
       "      <td>4</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103921</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>29</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>29</td>\n",
       "      <td>124</td>\n",
       "      <td>42</td>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2013</td>\n",
       "      <td>4</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date_block_num  shop_id  item_id  item_cnt_month  shop_city  \\\n",
       "1103919               4        2       27             0.0          0   \n",
       "1103920               4        2       28             0.0          0   \n",
       "1103921               4        2       29             0.0          0   \n",
       "\n",
       "         shop_name1  shop_type  item_category_id  name_1  name_2  name_3  \\\n",
       "1103919           0          5                19      27      77      42   \n",
       "1103920           0          5                30      28     108      42   \n",
       "1103921           0          5                23      29     124      42   \n",
       "\n",
       "         item_type  item_subtype  item_cnt_month_lag_1  item_cnt_month_lag_2  \\\n",
       "1103919          5            10                   0.0                   0.0   \n",
       "1103920          8            55                   0.0                   0.0   \n",
       "1103921          5            16                   0.0                   0.0   \n",
       "\n",
       "         item_cnt_month_lag_3  date_block_num_avg_item_cnt_lag_1  \\\n",
       "1103919                   0.0                           0.292969   \n",
       "1103920                   0.0                           0.292969   \n",
       "1103921                   0.0                           0.000000   \n",
       "\n",
       "         date_block_num_and_item_id_avg_item_cnt_lag_1  \\\n",
       "1103919                                       0.027771   \n",
       "1103920                                       0.111084   \n",
       "1103921                                       0.000000   \n",
       "\n",
       "         date_block_num_and_item_id_avg_item_cnt_lag_2  \\\n",
       "1103919                                       0.083313   \n",
       "1103920                                       0.083313   \n",
       "1103921                                       0.000000   \n",
       "\n",
       "         date_block_num_and_item_id_avg_item_cnt_lag_3  \\\n",
       "1103919                                       0.083313   \n",
       "1103920                                       0.194458   \n",
       "1103921                                       0.000000   \n",
       "\n",
       "         date_block_num_and_shop_id_avg_item_cnt_lag_1  \\\n",
       "1103919                                       0.076233   \n",
       "1103920                                       0.076233   \n",
       "1103921                                       0.000000   \n",
       "\n",
       "         date_block_num_and_shop_id_avg_item_cnt_lag_2  \\\n",
       "1103919                                       0.095459   \n",
       "1103920                                       0.095459   \n",
       "1103921                                       0.000000   \n",
       "\n",
       "         date_block_num_and_shop_id_avg_item_cnt_lag_3  \\\n",
       "1103919                                       0.063599   \n",
       "1103920                                       0.063599   \n",
       "1103921                                       0.000000   \n",
       "\n",
       "         date_block_num_and_shop_city_avg_item_cnt_lag_1  \\\n",
       "1103919                                         0.076233   \n",
       "1103920                                         0.076233   \n",
       "1103921                                         0.000000   \n",
       "\n",
       "         date_block_num_and_shop_name1_avg_item_cnt_lag_1  \\\n",
       "1103919                                          0.076233   \n",
       "1103920                                          0.076233   \n",
       "1103921                                          0.000000   \n",
       "\n",
       "         date_block_num_and_shop_type_avg_item_cnt_lag_1  \\\n",
       "1103919                                          0.29126   \n",
       "1103920                                          0.29126   \n",
       "1103921                                          0.00000   \n",
       "\n",
       "         date_block_num_and_item_category_id_avg_item_cnt_lag_1  \\\n",
       "1103919                                           0.743652        \n",
       "1103920                                           1.081055        \n",
       "1103921                                           0.000000        \n",
       "\n",
       "         date_block_num_and_item_type_avg_item_cnt_lag_1  \\\n",
       "1103919                                         0.616699   \n",
       "1103920                                         1.048828   \n",
       "1103921                                         0.000000   \n",
       "\n",
       "         date_block_num_and_item_subtype_avg_item_cnt_lag_1  \\\n",
       "1103919                                           0.682129    \n",
       "1103920                                           1.081055    \n",
       "1103921                                           0.000000    \n",
       "\n",
       "         date_block_num_and_shop_id_and_item_id_avg_item_cnt_lag_1  \\\n",
       "1103919                                                0.0           \n",
       "1103920                                                0.0           \n",
       "1103921                                                0.0           \n",
       "\n",
       "         date_block_num_and_shop_id_and_name_1_avg_item_cnt_lag_1  \\\n",
       "1103919                                                0.0          \n",
       "1103920                                                0.0          \n",
       "1103921                                                0.0          \n",
       "\n",
       "         date_block_num_and_shop_id_and_name_2_avg_item_cnt_lag_1  \\\n",
       "1103919                                           0.564453          \n",
       "1103920                                           0.347900          \n",
       "1103921                                           0.000000          \n",
       "\n",
       "         date_block_num_and_shop_id_and_name_3_avg_item_cnt_lag_1  \\\n",
       "1103919                                           0.096985          \n",
       "1103920                                           0.096985          \n",
       "1103921                                           0.000000          \n",
       "\n",
       "         date_block_num_and_shop_id_and_item_category_id_avg_item_cnt_lag_1  \\\n",
       "1103919                                           0.500000                    \n",
       "1103920                                           0.399902                    \n",
       "1103921                                           0.000000                    \n",
       "\n",
       "         delta_price_lag  item_shop_last_sale  item_last_sale  \\\n",
       "1103919         0.345459                    1               1   \n",
       "1103920         0.259521                   -1               1   \n",
       "1103921         0.000000                   -1               4   \n",
       "\n",
       "         item_first_sale  year  month  days  \n",
       "1103919                4  2013      4    31  \n",
       "1103920                4  2013      4    31  \n",
       "1103921                4  2013      4    31  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_pickle('/kaggle/input/all-datta/data2.pkl')\n",
    "# data = data[[\n",
    "#     'date_block_num', \n",
    "#     'shop_id', \n",
    "#     'item_id', \n",
    "#     'item_cnt_month',\n",
    "    \n",
    "# #     'shop_city', \n",
    "# #     'shop_name1',\n",
    "# #     'shop_type', \n",
    "    \n",
    "# #     'name_1', \n",
    "# #     'name_2', \n",
    "# #     'name_3',\n",
    "    \n",
    "# #     'item_type', \n",
    "# #     'item_subtype', \n",
    "# #     'item_category_id', \n",
    "    \n",
    "# #     'item_cnt_month_lag_1',\n",
    "# #     'item_cnt_month_lag_2', \n",
    "# #     'item_cnt_month_lag_3',\n",
    "# #     'date_block_num_avg_item_cnt_lag_1',\n",
    "# #     'date_block_num_and_item_id_avg_item_cnt_lag_1',\n",
    "# #     'date_block_num_and_item_id_avg_item_cnt_lag_2',\n",
    "# #     'date_block_num_and_item_id_avg_item_cnt_lag_3',\n",
    "# #     'date_block_num_and_shop_id_avg_item_cnt_lag_1',\n",
    "# #     'date_block_num_and_shop_id_avg_item_cnt_lag_2',\n",
    "# #     'date_block_num_and_shop_id_avg_item_cnt_lag_3',\n",
    "    \n",
    "# #     'date_block_num_and_shop_city_avg_item_cnt_lag_1',\n",
    "# #     'date_block_num_and_shop_name1_avg_item_cnt_lag_1',\n",
    "# #     'date_block_num_and_shop_type_avg_item_cnt_lag_1',\n",
    "    \n",
    "# #     'date_block_num_and_item_category_id_avg_item_cnt_lag_1',\n",
    "# #     'date_block_num_and_item_type_avg_item_cnt_lag_1',\n",
    "# #     'date_block_num_and_item_subtype_avg_item_cnt_lag_1',\n",
    "    \n",
    "# #     'date_block_num_and_shop_id_and_item_id_avg_item_cnt_lag_1',\n",
    "# #     'date_block_num_and_shop_id_and_name_1_avg_item_cnt_lag_1',\n",
    "# #     'date_block_num_and_shop_id_and_name_2_avg_item_cnt_lag_1',\n",
    "# #     'date_block_num_and_shop_id_and_name_3_avg_item_cnt_lag_1',\n",
    "# #     'date_block_num_and_shop_id_and_item_category_id_avg_item_cnt_lag_1',\n",
    "    \n",
    "# #     'delta_price_lag', \n",
    "# #     'item_shop_last_sale', \n",
    "# #     'item_last_sale',\n",
    "# #     'item_first_sale', \n",
    "# #     'year', \n",
    "# #     'month', \n",
    "# #     'days'\n",
    "# ]]\n",
    "\n",
    "print(data.shape)\n",
    "data.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2154.6486486486488, 2463.0, 2351.0810810810813, 2506.7297297297296, 2377.945945945946, 2258.7297297297296, 2416.7837837837837, 3432.815789473684, 2279.1052631578946, 2168.3684210526317, 2128.358974358974, 1720.1, 1770.275, 1814.35, 1673.4878048780488, 1881.8536585365853, 1645.6829268292684, 1702.439024390244, 1962.7560975609756, 2952.170731707317, 1940.0, 1661.2926829268292, 1621.9756097560976, 1397.8536585365853, 1351.9268292682927, 1354.3170731707316, 1371.439024390244, 1484.5853658536585, 1392.0731707317073, 1360.7380952380952]\n"
     ]
    }
   ],
   "source": [
    "##求出每个月的均值销售额##shop_mean_months\n",
    "a = []\n",
    "for i in range(3,34):\n",
    "    b = data[data.date_block_num==i]##取到每一个月\n",
    "    c = b.sum()['item_cnt_month']#求和\n",
    "    d = len(b.shop_id.unique())#长度（个数）\n",
    "    a.append(c/d)#求得均值\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from pylab import *                                 #支持中文\n",
    "\n",
    "# ax.plot(range(4,34), a, marker='o', mec='r', mfc='w',label='shop_mean_month')\n",
    "# ax.legend()  # 让图例生效\n",
    "\n",
    "plt.subplots(1,1,figsize=(14,10))\n",
    "plt.plot(range(4,34), a, marker='o', mec='r', mfc='w',label='shop_mean_month')\n",
    "plt.legend()  # 让图例生效\n",
    "plt.xlabel('month') #X轴标签\n",
    "plt.ylabel(\"shop_mean_month\") #Y轴标签\n",
    "plt.title(\"shop_mean_month\") #标题\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2334"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##添加周特征，周的每一天的特征\n",
    "weekarr = []\n",
    "t = 2\n",
    "count = 0\n",
    "for w in range(3):\n",
    "    for i in [31,28,31,30,31,30,31,31,30,31,30,31]:\n",
    "        a = [0,0,0,0,0,0,0,count]\n",
    "        count+=1\n",
    "        for j in range(i):\n",
    "            a[t]+=1\n",
    "            if t==6:\n",
    "                t=-1\n",
    "            t+=1\n",
    "        weekarr.append(a)\n",
    "weekarr = pd.DataFrame(np.vstack(weekarr), columns=['week0','week1','week2','week3','week4','week5','week6','date_block_num'])\n",
    "data = pd.merge(data, weekarr, on=['date_block_num'], how='left')#加进去\n",
    "del weekarr\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##数据集的划分\n",
    "X_zong = data.drop(['item_cnt_month'], axis=1)#去掉标签\n",
    "Y_train = data[data.date_block_num < 33]['item_cnt_month']#训练集的标签\n",
    "Y_valid = data[data.date_block_num == 33]['item_cnt_month']#交叉验证的标签\n",
    "del data##删除数据集减少占用内存\n",
    "gc.collect()##垃圾回收机制"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/sklearn/preprocessing/data.py:334: DataConversionWarning: Data with input dtype int8, float16, int16 were all converted to float64 by MinMaxScaler.\n",
      "  return self.partial_fit(X, y)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "28"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler ##数据归一化\n",
    "minMax = MinMaxScaler()    \n",
    "\n",
    "\n",
    "X_zong_std = minMax.fit_transform(X_zong.iloc[:,:-7])  ##取所有的行，除了后七列的所有的\n",
    "X_zong.iloc[:,:-7] = pd.DataFrame(np.vstack(X_zong_std),columns=X_zong.columns[:-7])\n",
    "X_zong.iloc[:,:-7] = downcast_dtypes(X_zong.iloc[:,:-7])#转换数据类型，为了减少内存\n",
    "\n",
    "##这就是排除出去的后七列\n",
    "X_zong['week0'] = X_zong['week0'].astype(np.int8)\n",
    "X_zong['week1'] = X_zong['week1'].astype(np.int8)\n",
    "X_zong['week2'] = X_zong['week2'].astype(np.int8)\n",
    "X_zong['week3'] = X_zong['week3'].astype(np.int8)\n",
    "X_zong['week4'] = X_zong['week4'].astype(np.int8)\n",
    "X_zong['week5'] = X_zong['week5'].astype(np.int8)\n",
    "X_zong['week6'] = X_zong['week6'].astype(np.int8)\n",
    "\n",
    "del X_zong_std\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = X_zong[X_zong.date_block_num < 0.96679688]\n",
    "X_valid = X_zong[X_zong.date_block_num == 0.96679688]\n",
    "X_test = X_zong[X_zong.date_block_num == 1]\n",
    "del X_zong\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 模型的训练--lightgbm、catboost ，集成（堆叠）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
      "  if getattr(data, 'base', None) is not None and \\\n",
      "/opt/conda/lib/python3.6/site-packages/xgboost/core.py:588: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
      "  data.base is not None and isinstance(data, np.ndarray) \\\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\tvalidation_0-rmse:1.14506\tvalidation_1-rmse:1.0526\n",
      "Multiple eval metrics have been passed: 'validation_1-rmse' will be used for early stopping.\n",
      "\n",
      "Will train until validation_1-rmse hasn't improved in 10 rounds.\n",
      "[1]\tvalidation_0-rmse:1.10231\tvalidation_1-rmse:1.00808\n",
      "[2]\tvalidation_0-rmse:1.06743\tvalidation_1-rmse:0.969801\n",
      "[3]\tvalidation_0-rmse:1.02698\tvalidation_1-rmse:0.936995\n",
      "[4]\tvalidation_0-rmse:0.994626\tvalidation_1-rmse:0.913135\n",
      "[5]\tvalidation_0-rmse:0.971882\tvalidation_1-rmse:0.894922\n",
      "[6]\tvalidation_0-rmse:0.948823\tvalidation_1-rmse:0.877142\n",
      "[7]\tvalidation_0-rmse:0.928786\tvalidation_1-rmse:0.863674\n",
      "[8]\tvalidation_0-rmse:0.912335\tvalidation_1-rmse:0.852181\n",
      "[9]\tvalidation_0-rmse:0.898306\tvalidation_1-rmse:0.841511\n",
      "[10]\tvalidation_0-rmse:0.886688\tvalidation_1-rmse:0.834299\n",
      "[11]\tvalidation_0-rmse:0.875826\tvalidation_1-rmse:0.82816\n",
      "[12]\tvalidation_0-rmse:0.867278\tvalidation_1-rmse:0.823972\n",
      "[13]\tvalidation_0-rmse:0.859452\tvalidation_1-rmse:0.819766\n",
      "[14]\tvalidation_0-rmse:0.852422\tvalidation_1-rmse:0.818024\n",
      "[15]\tvalidation_0-rmse:0.846369\tvalidation_1-rmse:0.814503\n",
      "[16]\tvalidation_0-rmse:0.84185\tvalidation_1-rmse:0.813795\n",
      "[17]\tvalidation_0-rmse:0.836908\tvalidation_1-rmse:0.811846\n",
      "[18]\tvalidation_0-rmse:0.831777\tvalidation_1-rmse:0.810217\n",
      "[19]\tvalidation_0-rmse:0.828202\tvalidation_1-rmse:0.809543\n",
      "[20]\tvalidation_0-rmse:0.824205\tvalidation_1-rmse:0.80833\n",
      "[21]\tvalidation_0-rmse:0.821091\tvalidation_1-rmse:0.807828\n",
      "[22]\tvalidation_0-rmse:0.817559\tvalidation_1-rmse:0.806982\n",
      "[23]\tvalidation_0-rmse:0.814141\tvalidation_1-rmse:0.806779\n",
      "[24]\tvalidation_0-rmse:0.809949\tvalidation_1-rmse:0.804435\n",
      "[25]\tvalidation_0-rmse:0.807882\tvalidation_1-rmse:0.804332\n",
      "[26]\tvalidation_0-rmse:0.805702\tvalidation_1-rmse:0.80415\n",
      "[27]\tvalidation_0-rmse:0.802856\tvalidation_1-rmse:0.804008\n",
      "[28]\tvalidation_0-rmse:0.800821\tvalidation_1-rmse:0.803147\n",
      "[29]\tvalidation_0-rmse:0.799112\tvalidation_1-rmse:0.80285\n",
      "[30]\tvalidation_0-rmse:0.797568\tvalidation_1-rmse:0.802882\n",
      "[31]\tvalidation_0-rmse:0.795225\tvalidation_1-rmse:0.805395\n",
      "[32]\tvalidation_0-rmse:0.793409\tvalidation_1-rmse:0.804946\n",
      "[33]\tvalidation_0-rmse:0.791565\tvalidation_1-rmse:0.804734\n",
      "[34]\tvalidation_0-rmse:0.788489\tvalidation_1-rmse:0.804304\n",
      "[35]\tvalidation_0-rmse:0.786533\tvalidation_1-rmse:0.803778\n",
      "[36]\tvalidation_0-rmse:0.785288\tvalidation_1-rmse:0.803452\n",
      "[37]\tvalidation_0-rmse:0.783522\tvalidation_1-rmse:0.803099\n",
      "[38]\tvalidation_0-rmse:0.781696\tvalidation_1-rmse:0.804609\n",
      "[39]\tvalidation_0-rmse:0.779874\tvalidation_1-rmse:0.804398\n",
      "Stopping. Best iteration:\n",
      "[29]\tvalidation_0-rmse:0.799112\tvalidation_1-rmse:0.80285\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "4745.937068462372"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##参数可以自己改\n",
    "ts = time.time()\n",
    "\n",
    "model = XGBRegressor(\n",
    "    max_depth=10,\n",
    "    n_estimators=1000,\n",
    "    min_child_weight=0.5, \n",
    "    colsample_bytree=0.9, \n",
    "    subsample=0.8, \n",
    "    eta=0.1,    \n",
    "    seed=1)\n",
    "\n",
    "model.fit(\n",
    "    X_train, \n",
    "    Y_train, \n",
    "    eval_metric=\"rmse\", \n",
    "    eval_set=[(X_train, Y_train), (X_valid, Y_valid)], \n",
    "    verbose=True, \n",
    "    early_stopping_rounds = 10)\n",
    "\n",
    "time.time() - ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f02dbc4db38>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1008 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_features(model, (10,14))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 保存为自己所需要的数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_test = model.predict(X_test).clip(0, 20)##clip0-20之间\n",
    "test = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/test.csv').set_index('ID')\n",
    "\n",
    "submission = pd.DataFrame({\n",
    "    \"ID\": test.index, \n",
    "    \"item_cnt_month\": Y_test\n",
    "})\n",
    "submission.to_csv('xgb_submission1.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 自己先查资料、了解地震波的原理，以及以报告的形式，写出地震比赛中的要求以及评估方式，自己可以去探索一下数据（EDA）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
